Project Team:
Nathan Alexander (Howard University)
Anika Hobson (Nubian Hueman)
Zoe Williams (Howard University)
Qyana Stewart (Howard University)
Whatever happened to Chocolate City, the term used to refer to Washington DC’s Black population?
In this short report, we discuss some key historical and contemporary connections and then we analyze US census data to examine historical and continual shifts in Washington DC’s Black population. We focus on racial isolation and residential segregation. We close with a discussion on how modern critical theories, mathematics, and population-level data can increase our general knowledge as a community and inform our perspectives on local policies and social action issues, like voting and community service.
The materials presented here are for the October 2024 workshop at Nubian Hueman studios in DC.
The phrase “Chocolate City” has long been used to describe Washington, D.C.’s significant Black population and vibrant Black communities. However, in recent decades, the appropriateness of this label has come into question due to shifting demographics and changing urban dynamics.
Washington, D.C. reached its peak as a majority-Black city in 1970, when African Americans comprised 71% of the population. Since then, the city has experienced a steady decline in its Black population. By 2015, DC’s population of Black residents had decreased to 48% and the current population is 44%.
Several factors have contributed to the erosion of D.C.’s status as a “Chocolate City”:
Gentrification: Rising property values and cost of living have pushed many long-time Black residents out of traditionally African American neighborhoods
Suburban migration: Many middle-class Black families have moved to more affordable suburbs in Maryland and Virginia
Urban redevelopment: The replacement of public housing projects with mixed-income developments has altered the demographic makeup of some areas
Despite increasing diversity, Washington, D.C. remains highly segregated. The Black-White segregation index stood at 70 in 2015, showing only modest improvement from 77 in 1980. This suggests that while the overall racial composition of the city has changed, residential patterns of segregation persist, often isolating poor Black residents.
The “Chocolate City” moniker continues to hold cultural significance, even as its demographic accuracy wanes. It represented not just a statistical majority, but also the city’s important role in Black history, culture, and political aspirations. As Washington, D.C. evolves, the question remains whether the “Chocolate City” label will continue to resonate or if new descriptors will emerge to capture the city’s changing identity.
As of 2022, the racial composition of Washington, D.C. has changed considerably:
Black or African American (Non-Hispanic): 43.5%
White (Non-Hispanic): 36.3%
Hispanic (of any race): 4.05%
Asian (Non-Hispanic): 3.95%
Two or more races (Non-Hispanic): 3.94%
This shift represents a significant change from the city’s historical Black majority.
In this workshop, we examine the various quantitative and historical factors that have contributed race and isolation in DC.
The discussions and analysis in this report are informed by the US
Census microdata, which was accessed through the
tidycensus() package in R. The American Community Survey
(ACS) Public Use Microdata Sample (PUMS) is used to analyze
pre-aggregate data at a local level. This data allowed us to make
various custom estimates that may not be normally available by the US
Census Bureau.
Learn more about PUMS here.
In our data, “totalpopE” is a single number representing the estimated population, while “totalpopM” represents a range around that estimate within which the true population is likely to fall. The designation for “E” and “M” follows in line as the estimate and the margin of error.
## Getting data from the 2016-2020 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## Getting data from the 2016-2020 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## Getting data from the 2016-2020 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## [1] 0.10294295 0.10851649 0.08528862 0.07015620 0.05529833 0.18958024
## Simple feature collection with 206 features and 28 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -77.11976 ymin: 38.79165 xmax: -76.9094 ymax: 38.99511
## Geodetic CRS: NAD83
## # A tibble: 206 × 29
## GEOID.x totalpopE pwhite pblack pasian phisp whiteE blackE asianE hispanicE
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 11001001… 3489 67.3 6.05 8.08 12.6 2348 211 282 441
## 2 11001002… 4104 33.4 25.4 7.94 28.6 1371 1041 326 1175
## 3 11001003… 3825 41.8 25.8 3.22 24.1 1598 987 123 921
## 4 11001004… 4785 82.2 4.76 2.70 7.57 3932 228 129 362
## 5 11001010… 3927 54.6 28.8 3.54 8.71 2143 1130 139 342
## 6 11001004… 3169 42.1 43.7 4.64 5.93 1335 1384 147 188
## 7 11001008… 3376 21.6 59.1 1.66 14.0 730 1996 56 472
## 8 11001009… 1889 3.55 79.0 0.212 15.7 67 1493 4 296
## 9 11001009… 3575 8.87 80.9 0 7.55 317 2893 0 270
## 10 11001005… 2723 72.8 3.12 14.1 8.52 1981 85 384 232
## # ℹ 196 more rows
## # ℹ 19 more variables: incomeE <dbl>, STATEFP <chr>, PLACEFP <chr>,
## # PLACENS <chr>, AFFGEOID <chr>, GEOID.y <chr>, NAME <chr>, NAMELSAD <chr>,
## # STUSPS <chr>, STATE_NAME <chr>, LSAD <chr>, ALAND <dbl>, AWATER <dbl>,
## # geometry <POLYGON [°]>, white.tot <dbl>, asian.tot <dbl>, black.tot <dbl>,
## # hisp.tot <dbl>, tpopc <dbl>
## Getting data from the 2016-2020 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## [1] 0.04797083 0.04003167 0.03181145 0.02965383 0.01987875 0.17114119
## New names:
## • `` -> `...1`
## • `` -> `...2`
## # A tibble: 6 × 2
## Black Hispanic
## <dbl> <dbl>
## 1 0.103 0.0480
## 2 0.109 0.0400
## 3 0.0853 0.0318
## 4 0.0702 0.0297
## 5 0.0553 0.0199
## 6 0.190 0.171